NLP | ACL·EMNLP·NAACL·EACL·COLING 학회 완전 비교
자연어처리분야 글로벌 탑티어 학회인 ACL·EMNLP·NAACL·EACL·COLING 에 대해 대해 종합적으로 비교 분석합니다. 이 글은 NLP 연구를 준비하는 대학원생들이 본인의 연구 주제와 목표에 따라 어떤 학회를 겨냥할지 전략적으로 판단하는 데 도움을 주는 것을 목적으로 합니다.
Murfy AI Blog6 min read
NLP
32 stories
Exploring the TextAttack Framework: Components, Features, and Practical Applications
In this tutorial, we look at the TextAttack framework for NLP data augmentation, adversarial training, and adversarial attacks.
Paperspace Blog13 min readEnhancing NLP Models for Robustness Against Adversarial Attacks: Techniques and Applications
Learn about a variety of techniques used to keep deep learning NLP models secure.
Paperspace Blog14 min read
Extrinsic Hallucinations in LLMs
Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somewhat generalized to cases when the model makes mistakes.
Lil'Log (Lilian Weng)25 min read
Do text embeddings perfectly encode text?
'Vec2text' can serve as a solution for accurately reverting embeddings back into text, thus highlighting the urgent need for revisiting security protocols around embedded data.
The Gradient9 min read
NLP Research in the Era of LLMs
NLP research has undergone a paradigm shift over the last year. A range of large language models (LLMs) has validated the unreasonable effectiveness of scale1.
NLP News (Sebastian Ruder)13 min read
Adversarial Attacks on LLMs
The use of large language models in the real world has strongly accelerated by the launch of ChatGPT. We (including my team at OpenAI, shoutout to them) have invested a lot of effort to build default safe behavior into the model during the alignment process (e.g. via RLHF).
Lil'Log (Lilian Weng)31 min read
LLM Powered Autonomous Agents
Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples.
Lil'Log (Lilian Weng)28 min read
Prompt Engineering
Prompt Engineering, also known as In-Context Prompting, refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights.
Lil'Log (Lilian Weng)20 min readPaLM 🌴, DALL-E 2 👨🎨, Chinchilla 🐭, Chain-of-thought prompting ⛓💭✍️, Values and Culture in NLP 🏛
Hi all,This newsletter covers PaLM, DALL-E 2, and Chinchilla, chain-of-thought prompting, and the role of values and culture in NLP.This edition is somewhat delayed as I've been busy with planning a move (I'll be flying 🛫 to Germany tomorrow; say hi 👋 if you're in Berlin) and exhausted by current…
NLP News (Sebastian Ruder)9 min read
Pre-training + Massive Multi-tasking, Benchmarking in NLP, EMNLP primer, 🤗 NLP Course, ACL 2021 recap,
Hi all, First off, some personal news: I've moved from DeepMind to Google Research this week. Because of this move, the past months have been quite busy. In light of this, I decided to pause the newsletter over the last couple of months.
NLP News (Sebastian Ruder)8 min read
Reducing Toxicity in Language Models
Large pretrained language models are trained over a sizable collection of online data. They unavoidably acquire certain toxic behavior and biases from the Internet. Pretrained language models are very powerful and have shown great success in many NLP tasks.
Lil'Log (Lilian Weng)21 min read
Controllable Neural Text Generation
[Updated on 2021-02-01: Updated to version 2.0 with several work added and many typos fixed.] [Updated on 2021-05-26: Add P-tuning and Prompt Tuning in the “prompt design” section.] [Updated on 2021-09-19: Add “unlikelihood training”.]
Lil'Log (Lilian Weng)42 min read
How to Build an Open-Domain Question Answering System?
[Updated on 2020-11-12: add an example on closed-book factual QA using OpenAI API (beta). A model that can answer any question with regard to factual knowledge can lead to many useful and practical applications, such as working as a chatbot or an AI assistant🤖.
Lil'Log (Lilian Weng)32 min read
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